Locally Weighted Regression for Estimating and Smoothing ODF Field Simultaneously

被引：0

作者：

Liu, Xiaozheng ^{[1
,2
,3
,4
,5
]}

Yang, Guang ^{[1
,2
,3
]}

Peterson, Bradley S. ^{[4
,5
]}

Xu, Dongrong ^{[1
,2
,3
,4
,5
]}

机构：

[1] Minist Educ, Key Lab Brain Functional Genomics, Shanghai 20062, Peoples R China

[2] East China Normal Univ, Shanghai Key Lab Brain Functional Genomics, Beijing 20062, Peoples R China

[3] East China Normal Univ, Shanghai Key Lab Magnet Resonance, Beijing 20062, Peoples R China

[4] New York State Psychiat Inst & Hosp, NYSPI Unit 74, New York, NY 10032 USA

[5] Columbia Univ, MRI Unit, Dept Psychiat, 1051 Riverside Dr, New York, NY 10032 USA

来源：

MEDICAL IMAGING AND AUGMENTED REALITY | 2010年 / 6326卷

关键词：

DIFFUSION; RESOLUTION;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

High angular resolution diffusion imaging (HARDI) has become an important tool for resolving neural architecture in regions with complex patterns of fiber crossing. A popular method for estimating the diffusion orientation distribution function (ODF) employs a least square (LS) approach by modeling the raw HARDI data on a spherical harmonic basis. We propose herein a novel approach for reconstruction of ODF fields from raw HARDI data that combines into one step the smoothing of raw HARDI data and the estimation of ODF field using correlated information in a local neighborhood. Based on the most popular method of least square for estimating ODF, we incorporated into it local weights that are determined by a special weighting function, making it a locally weighted linear least square method (LWLLS). The method thus can efficiently perform the smoothing of HARDI data and estimating the ODF field simultaneously. We evaluated the effectiveness of this method using both simulated and real-world HARDI data.

引用

页码：211 / +

页数：2

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